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dspy-ruby

Define typed signatures and modular components for LLM-driven Ruby applications.

Updated Mar 26, 2026
Search Tags:#llm#tooling#modules#ruby#signatures#dsl
One-click install
npx skills add https://github.com/jaydubya818/New_baseline --skill dspy-ruby-jaydubya818
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy-ruby
Source: https://github.com/jaydubya818/New_baseline/tree/main/skills/compound-engineering/plugins/compound-engineering/skills/dspy-ruby
Command: npx skills add https://github.com/jaydubya818/New_baseline --skill dspy-ruby-jaydubya818

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) and references (resource) components.

What problem does it solve?

Define and enforce structured interfaces for LLM-driven Ruby applications using typed signatures and modular components.

Core Features & Use Cases

  • Type-safe signatures: Use Sorbet-based inputs/outputs to generate reliable JSON schemas for LLM interactions.
  • Modular design: Compose signatures, modules, agents, and tools to create scalable, maintainable AI workflows.
  • Observability & optimization: Integrates with Langfuse/OpenTelemetry, storage, and optimization tooling for production-grade pipelines.
  • Use cases: Build end-to-end AI apps with multi-provider LM support, tool orchestration, and robust testing in Ruby.

Quick Start

Install the DSPy.rb gem, scaffold a basic signature, wire a simple predictor, and observe typed results in your Ruby app.

Frequently Asked Questions about dspy-ruby

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build type-safe LLM applications in Ruby?▼

Build type-safe LLM applications in Ruby by using Sorbet-based typed signatures to define structured inputs and outputs, which generate reliable JSON schemas for predictable model interactions and robust AI workflows.

What is the best way to structure LLM workflows in a Ruby on Rails app?▼

Structure LLM workflows in a Ruby on Rails app by composing modular signatures, modules, and tools using domain-specific DSLs, enabling scalable and maintainable AI pipeline architecture.

Can I use multiple LLM providers in a Ruby application?▼

Yes, you can integrate multiple LLM providers in a Ruby application through multi-provider LM support, allowing you to orchestrate tools and switch between different language models seamlessly.

How do I enforce structured JSON output from LLMs in Ruby?▼

Enforce structured JSON output from LLMs in Ruby by defining Sorbet-typed signatures that automatically generate and enforce strict JSON schemas during language model interactions.

Does DSPy.rb support observability and optimization for production AI pipelines?▼

DSPy.rb supports observability and optimization for production AI pipelines through integrations with Langfuse, OpenTelemetry, and built-in storage tooling to monitor and refine LLM applications.

Do I need Sorbet to define typed signatures for LLM interactions?▼

Yes, Sorbet is required to define typed signatures for LLM interactions, as it provides the type-safe inputs and outputs necessary to generate reliable JSON schemas for modular AI components.

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